UAV Recognition and Detection Based on Multi-scale Feature Fusion
CAO Jinghao1， ZHANG Junju1， HUANG Wei1， YAO Ruotong1， ZHANG Ping2
1. School of Electronic and Optical Engineering， Nanjing University of Science and Technology， Nanjing 210094， Jiangsu， China； 2. Jiangsu North Lake Optoelectronics Co. ， Ltd. ， Yangzhou 225009， Jiangsu， China
Abstract：In order to realize the UAV target recognition and detection in complex background, an improved Faster R-CNN network model is proposed. Aiming at the difference between the target size in the field of view and the labeled training dataset, three different scales of receptive field parallel detection and weight sharing feature extraction structure Tri-VGG network are designed, and a training strategy is designed to avoid network over fitting. The optimized network detection average precision (average precision, AP) reaches 90. 9%, and the real-time performance reaches 24 frames per second, which meets actual needs.
